The　reliability　of　semiconductor　devices　is　a　key　indicator　to　measure　the　reliability　of　electronic　devices.　In　view　of　the　difficulties　in　modeling,　low　prediction　accuracy　and　long　prediction　period　faced　by　traditional　reliability　prediction　methods　of　semiconductor　devices,　a　deep　learning　based　reliability　prediction　method　of　semiconductor　devices　is　proposed　in　this　paper.　Besides,　the　accelerated　degradation　test　data　set　of　bipolar　transistors　under　constant　stress　of　temperature　and　humidity　is　analyzed,　and　the　failure　sensitive　parameter　Icbo　of　transistors　is　determined.　The　Data　Fitting,　LSTM,　GRU　and　GRU-LSTM　models　are　used　to　predict　the　trend　of　Icbo　degradation　of　three　transistors　which　are　randomly　selected　from　data　set.　The　prediction　results　of　device　storage　life　using　data　fitting　method　and　GRU-LSTM　model　are　compared,　and　it　is　found　that　the　overall　distribution　of　device　storage　life　predicted　by　the　two　methods　is　similar,　but　the　prediction　accuracy　of　GRU-LSTM　model　is　higher　and　more　suitable　to　the　actual　situation.　This　paper　can　provide　some　reference　for　predicting　the　reliability　of　domestic　semiconductor　devices.　©　2024　SPIE.　All　rights　reserved.